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The Status of Hunger and Malnutrition in Zambia:

A Review of Methods and Indicators

By

Rhoda Mofya Mukuka and Musonda Mofu

Technical Paper No. 5

June, 2016.

Indaba Agricultural Policy Research Institute (IAPRI) Lusaka, Zambia Downloadable at: http://www.iapri.org.zm or http://www.aec.msu.edu/fs2/zambia/index.htm

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The Status of Hunger and Malnutrition in Zambia:

A Review of Methods and Indicators

By

Rhoda Mofya Mukuka and Musonda Mofu

Technical Paper No. 5

June, 2016

Indaba Agricultural Policy Research Institute (IAPRI)

26a Middleway, Kabulonga,

Lusaka, Zambia

Rhoda Mofya-Mukuka is Research Fellow at Indaba Agricultural Policy Research Institute

and Musonda Mofu is Deputy Director at National Food and Nutrition Commission of

Zambia

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ACKNOWLEDGEMENTS

The Indaba Agricultural Policy Research Institute (IAPRI) is a non-profit company limited by

guarantee and collaboratively works with public and private stakeholders. IAPRI exists to

carry out agricultural policy research and outreach, serving the agricultural sector in Zambia

so as to contribute to sustainable pro-poor agricultural development.

This technical report was carried out in partnership with the National Food and Nutrition

Commission (NFNC) in consultation with the Civil Society Organisation for Scaling up

Nutrition (CSO-SUN), the Central Statistical Office (CSO) and the Food and Agriculture

Organisation (FAO) office in Zambia. The authors are grateful to the Ministry of Agriculture

(MoA) through the Permanent Secretary’s office for requesting IAPRI and NFNC to carry

out this study.

The authors acknowledge the financial and substantive continued support of the Embassy of

Sweden and the United States Agency for International Development (USAID) in Lusaka.

Any views expressed or remaining errors are solely the responsibility of the authors.

Comments and questions should be directed to:

The Executive Director

Indaba Agricultural Policy Research Institute

26A Middleway, Kabulonga,

Lusaka.

Telephone: +260 211 261194

Telefax: +260 211 261199

Email: info@iapri.org.zm.

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INDABA AGRICULTURAL POLICY RESEARCH INSTITUTE

TEAM MEMBERS

The Zambia-based IAPRI research team is comprised of Antony Chapoto, Brian Chisanga,

Cliff Dlamini, Munguzwe Hichaambwa, Chance Kabaghe, Stephen Kabwe, Auckland

Kuteya, Rhoda Mofya-Mukuka, Thelma Namonje, Paul Chimuka Samboko, Ballard Zulu,

and Olipa Zulu. MSU-based researchers associated with IAPRI are Eric Crawford, Steven

Haggblade, Thomas S. Jayne, Nicole Mason, Chewe Nkonde, Nicholas Sitko, Melinda

Smale, and David Tschirley.

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EXECUTIVE SUMMARY

Hunger, undernourishment, and malnutrition rates for Zambia have been reported in different

publications as being extremely high and among highest rates in the world. The most recent

statistics on undernourishment by Food and Agriculture Organisation (FAO), International

Fund for Agricultural Development (IFAD) and World Food Programme (WFP) (2014)

ranked Zambia as having the highest levels in Africa and second from the bottom in world at

48%, a figure which is projected for 2014 to 2016. The MoA, is among key stakeholders,

which have queried these statistics given that Zambia recently experienced back to back

surpluses in maize production which provides 70 percent energy supply requirement. This

technical paper reviews the status of undernourishment, hunger and malnutrition in Zambia,

examining how the rates have been calculated. It also explains the likely causes of the high

rates of hunger and malnutrition in Zambia.

Secondary data and published survey reports were used to examine the data and methods

used to calculate Undernourishment, Hunger index, Food provisions and Malnutrition. To

assess the levels and track trends in nutritional status for Zambia, the study also used results

generated from the Zambia Demographic Health Survey (ZDHS) and the Living Conditions

Monitoring Survey (LCMS). ZDHS and LCMS are the only national level surveys which

provide data on nutrition and since they are not conducted in the same years, each one of

them is an important alternative data source.

The paper found some methodological issues in the calculation of undernourishment by FAO,

which is also a component in the calculation of the hunger index. For example, the level of

undernourishment calculated by FAO was reported as a population estimate, and hence

ignores the variation that may exist in the country.

However, the undernourishment rates are similar with those generated from

IAPRI/MAL/CSO survey, IFPRI Hunger Index and the figures from ZDHS. All the rates

range between 40% and 48%, an indication that hunger and malnutrition are likely to be at

alarmingly high rates, particularly at lean times of the year around September to February.

For Zambia to improve food security and reduce hunger, it should ensure that children and

their families have access to enough diverse and good-quality foods, clean water and safe

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sanitation. In addition, there is need child care capacity building programs at community and

household level through different Government and non-government interventions. On the

other hand, one reason Zambia may have ranked poorly in the State of Food Insecurity

(SOFI) report compared to other similar countries is lack of good information on food

consumption at household level. There is need to update the data on household food

consumption levels.

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TABLE OF CONTENTS

ACKNOWLEDGEMENTS ........................................................................................................ i

EXECUTIVE SUMMARY ..................................................................................................... iii

Table of Contents ....................................................................................................................... v

List of Tables ............................................................................................................................ vi

List of Figures ........................................................................................................................... vi

1. Introduction ........................................................................................................................ 1

2. Status, Measures and Indicators of Undernourishment, Hunger and Malnutrition ........ 2

2.1 Definitions of Undernourishment/Hunger and Malnutrition ...................................... 2

2.2 Status of Undernourishment ........................................................................................ 2

2.2.1 Measures and Indicators of Undernourishment ......................................................... 4

2.3 The Global Hunger Index – Zambia Status ...................................................................... 4

2.3.1 Global Hunger Index - Measures and Indicators ....................................................... 5

2.4 State of Malnutrition ........................................................................................................ 7

2.4.1 Indicators for measuring Malnutrition ....................................................................... 9

3. Review of HUNGER AND MALNUTRITION Ratings for Zambia ................................. 13

3.2 What do alternative data sources say about undernutrition in Zambia ..................... 14

3.3 What the Nutrition Index Means for Zambia ................................................................. 18

3.3.1 Determinants of Nutrition Status ............................................................................. 18

4. Conclusions and Recommendations .................................................................................... 20

References ................................................................................................................................ 22

Annex 1: Nutrition Index and the Challenges that are measured. ........................................... 26

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LIST OF TABLES

TABLE PAGE

Table 1: Dimensions and Indicators for Measuring GHI ........................................................... 6

Table 2: Severity of Hunger ....................................................................................................... 7

Table 3: Anthropometry Index and Challenges Measures ....................................................... 26

Table 4: Severity of Malnutrition per Indicator ....................................................................... 26

LIST OF FIGURES

FIGURE PAGE

Figure 1: Level of Undernourishment by Country in Africa ..................................................... 3

Figure 2: Level of Undernourished Population by Continent .................................................... 3

Figure 3: Global Hunger Index by Country in Africa................................................................ 5

Figure 4: Trends in Stunting, Wasting and Underweight. 1992 to 2013-14 .............................. 7

Figure 5: Stunting Rates by Province ........................................................................................ 8

Figure 6: Stunting Rates for Eastern Province ........................................................................... 9

Figure 7: Percent of Households Experiencing hunger in 2012 and 2015 .............................. 15

Figure 8: Hunger rates among Small Holder by Province in 2012 and 2015 (Among

Households Experiencing Hunger in at Least one Month) ...................................................... 15

Figure 9: Percentage of Households Experiencing Hunger by Month 2012 (Among

Households Experiencing Hunger in at least one Month) ....................................................... 16

Figure 10: Percentage of Households Experiencing Hunger by Month in 2015 (Among

Households Experiencing Hunger in at least one month) ........................................................ 17

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1. INTRODUCTION

This report outlines the findings and recommendations of a team of experts constituted by

MoA to investigate the state of food security and undernutrition in Zambia and provide a

detailed and accurate account of the situation as it is. The team was set up to primarily probe

questionable statistics used as the basis of a presentation made during the IFPRI/IAPRI/CSO-

SUN dialogue meeting on Food and Nutrition Security held in Lusaka on the 8th of December

2015. Drawing statistics from the 2014 FAO/IFAD/WFP SOFI report, the presentation

showed that Zambia had the worst food security status in Africa and ranked second in the

world, with 48 per cent of the population being malnourished or food insecure. These

citations were contrary to the fact that Zambia had produced food surpluses for most of years

in the past decade, meeting 70 percent energy supply requirement from staples. According to

the MoA this paradox required independent verification by a broad-based team of experts.

The team, which was coordinated by IAPRI and drew expertise from NFNC, CSO-SUN,

Food and FAO and the CSO was also mandated to examine the claim in the 2014 DHS report

that about 40 per cent of children under the age of five in Zambia were stunted. This report

outlines the findings and recommendations of the team.

Various published survey reports and research articles were used as a basis for discussion and

for assessing the methods for measuring undernourishment, hunger and malnutrition as

reported in the SOFI report, the Global Hunger Index and the 2013/14 ZDHS report on

nutrition. It also uses results from other ZDHS and the LCMS reports over the years to assess

the trends and the current status.

The rest of the paper is organized as follows: Section two discusses the measures and

indicators of undernourishment/hunger and malnutrition; section three reviews the ranking

for Zambia and; the conclusions are presented in section four.

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2. STATUS, MEASURES AND INDICATORS OF

UNDERNOURISHMENT, HUNGER AND MALNUTRITION

In this section, we discuss in detail the undernourishment/hunger and nutrition status in

Zambia and how the indicators are calculated. The sections first defines what

undernourishment is and its definition differs with that of malnutrition.

2.1 Definitions of Undernourishment/Hunger and Malnutrition

Undernourishment and malnutrition represent two different but interrelated human outcomes.

The former is a term used to denote lack of sufficient calories (energy) in the diet, and is

therefore synonymous with hunger (FAO, IFAD and WFP, 2014). According to FAO, it is

the deprivation of food with calorie consumption of less than 1,8000F

1 kilocalories per day,

which is the minimum that most people require to live a healthy and productive life (FAO,

IFAD and EFP, 2014). On the other hand, malnutrition is defined as the state that develops

when the body does not get the right amount of the vitamins, minerals and other nutrients it

needs to maintain a healthy body.1F

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2.2 Status of Undernourishment

In the FAO, IFAD and WFP (2014) SOFI report, Zambia was reported as having around 48%

of its population undernourished (hungry) as shown in Figure 1. Currently, the global average

of undernourishment is 11.3% and 23.8% in Africa. With an estimated undernutrition rate of

48%, Zambia is far worse than most countries and as ranks as the worst in Africa and second

from the bottom worldwide (FAO, IFAD and WFP, 2014). The 2014 report has SOFI

projects for a period of three year 2014-2016. Figure 2 shows the regional averages clearly

indicating that Africa rates have been and continue to be the worst in the world.

1 This value can range from 1,650 to more than 1,900 kilocalories per person per day for developing countries in 2014–2016. Each country’s average minimum energy requirement for low physical activity is used to estimate undernourishment (FAO/IFAD/WFP 2014). 2 http://medical-dictionary.thefreedictionary.com/malnutrition

3

Figure 1: Level of Undernourishment by Country in Africa

Source: FAO, IFAD & WFP, 2014

Figure 2: Level of Undernourished Population by Continent

Source: FAO, IFAD & WFP, 2014

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2.2.1 Measures and Indicators of Undernourishment

According to the FAO, IFAD and WFP (2014), undernourishment is calculated using the

dietary energy consumption (DEC) and dietary energy requirement (DER) as random

variables to estimate undernourishment. Both the DEC and the DER are expressed as

kilocalorie intake per person per day. When DEC is equal to DER, then this would be

regarded as a state of adequate nourishment. However when the dietary energy consumption

is less than the dietary energy requirement (DER>DEC), then this is classified as a state of

undernourishment.

Therefore, the prevalence of undernourishment is measured as the proportion of individuals

in a population with DEC below the individuals’ respective DERs (Naiken, 2014). FAO

collects DEC information from individual countries based on food consumption data

collected in national consumption/expenditure surveys. However, DER is not an observed

variable but a normatively derived measure that is subject to random variation (Naiken,

2014).

2.3 The Global Hunger Index – Zambia Status

The International Food Policy Research Institute (IFPRI) calculates the Global Hunger Index

(GHI) every year in order to keep track on the progress or regression of hunger in the world.

According to the 2015 Global Hunger Report, the GHI for Zambia is 41.1% and it is among

the three highest rates of Hunger in Africa and in the world (Figure 3). 2F

3

3 The GHI for Burundi, Comoros, the Democratic Republic of the Congo, Eritrea, Papua New Guinea, South Sudan, Sudan, and Syria, has not been calculated due to lack of data on undernourishment (IFPRI, WHH, Concern World Wide, 2015).

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Figure 3: Global Hunger Index by Country in Africa

Source: IFPRI, Welthungerhilfe & Concern Worldwide, 2015

2.3.1 Global Hunger Index - Measures and Indicators

Unlike the SOFI report, the GHI takes cognisance of the fact that hunger is a

multidimensional problem and therefore combines the following components in the

calculation of the index:

undernourishment which is as measured in the SOFI report discussed above. It is

basically a share of the population with insufficient caloric intake;

child wasting: the proportion of children under the age of five who suffer from wasting

(that is, low weight for their height, reflecting acute undernutrition);

child stunting: the proportion of children under the age of five who suffer from stunting

(that is, low height for their age, reflecting chronic undernutrition); and

child mortality: the mortality rate of children under the age of five. (partially reflecting

the fatal synergy of inadequate nutrition and unhealthy environments)

Previously, underweight was included instead of wasting and stunting, but this has since been

revised. In part this is because a child may have a normal weight, or even be overweight for

its age, but may be stunted (IFPRI, Welthungerhilfe, Concern Worldwide, 2015).

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To arrive at the GHI, each of the four components is first given standardised score based on

the thresholds which are set slightly higher than the country-level highest value observed

world-wide for that indicator between 1988 and 2013 (IFPRI, Welthungerhilfe, Concern

Worldwide, 2015). The standardised scores are then aggregated to calculate the GHI score for

each country. Each indicator is given a weighted such that undernourishment and child

mortality each contribute one-third of the GHI score, while the child undernutrition indicators

(child wasting and child stunting) each contribute one-sixth of the score. Table 3 shows the

components, the weighting of each component and what each component measures.

Table 1: Dimensions and Indicators for Measuring GHI

Dimensions Indicator Weight Reasons for inclusion

Inadequate food

supply (FAO)

Undernourishment

1/6 Measures insufficient food supply, an important

indicator of hunger

Refers to the entire population, both

children and adults

Used as a lead indicator for

international hunger targets

Child

undernutrition

(UNICEF, WHO,

World Bank)

Wasting

1/3 Goes beyond calorie availability, considers aspects of

diet quality and utilisation

Children are particularly vulnerable to nutritional

deficiencies

Is sensitive to uneven distribution of food within the

household

Stunting and wasting are the suggested nutrition

indicators for the Sustainable Development Goals

(SDGs)

Stunting

1/3

Child mortality

(Inter-agency

Group for Child

Mortality

Estimation

(IGME))

Under-five

mortality rate

1/3 Death is the most serious consequence of hunger, and

children are most vulnerable

Improves the GHI’s ability to reflect micronutrient

deficiencies

Wasting and stunting only partially capture the

mortality risk of undernutrition

Source: IFPRI, Welthungerhilfe & Concern Worldwide, 2015

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This calculation results in GHI scores on a 100-point scale where 0 is the best score (no

hunger) and 100 the worst. Table 2 shows the severity of hunger from low to extremely

alarming levels. Zambia, with a rate of 41.1% falls in the category of alarming rates.

Table 2: Severity of Hunger

< = 9.9 10 to 19.9 20.0 – 34.9 35 – 49.9 > = 50.0

Low Moderate Serious Alarming Extremely Alarming

Source: IFPRI, Welthungerhilfe and Concern Worldwide, 2015

2.4 State of Malnutrition

In terms of malnutrition, the most recent Zambia Demographic and Health Survey (ZDHS),

carried out in 2013/14, revealed that 40% of children under the age of five were stunted, 5%

wasted, 15% underweight, and 9% of children were estimated to be overweight. At 40%

stunting rates, Zambia’s malnutrition levels are among the highest in the world. However, the

rates have shown a reduction trend in the last 10 years has shown in Figure 4.

In the case of women, the situation exemplifies the double-burden of malnutrition, with 10%

(8% urban and 11% rural) underweight (BMI <18.5 kg/m2), and 19% (30% urban and 11%

rural) overweight or obese (BMI ≥ 25.0 kg/m2) (CSO, 2015).

Figure 4: Trends in Stunting, Wasting and Underweight. 1992 to 2013-14

Source: LCMS and DHS Various years

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Despite the agricultural growth that the country has experienced during in the last decade,

levels of malnutrition in the form of stunting, underweight and wasting have barely changed

in the population since 1992 and earlier. In 2013-14, five out of the ten provinces of Zambia

(Northern, Eastern, Luapula, Muchinga and Central provinces) had stunting rates above the

national average Lusaka, Western and Copper-belt provinces have the lowest rates as shown

in Figure 5.

Another study on nutrition status in five districts in Eastern province that was carried out by

USAID’s Feed the Future project in 2012 found similar stunting rates. The average rate for

the five districts was 46% as shown in Figure 6.

Figure 5: Stunting Rates by Province

42.5

36.2

43.3

43

35.7

43.6

48.5

36.9

37.2

36.2

40.31

0 10 20 30 40 50 60

Central

Copperbelt

Eastern

Luapula

Lusaka

Muchinga

Northen

North Western

Southern

Western

National

Source: DHS 2013-14

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Figure 6: Stunting Rates for Eastern Province

38.6

46.4

51.847.2

49.546.5

0.0

10.0

20.0

30.0

40.0

50.0

60.0

Chipata Katete Lundazi Nyimba Petauke Total

%

Total Percentage of stunted growth

Source: USAID Feed the Future Population Based Survey

2.4.1 Indicators for measuring Malnutrition

Anthropometric measurements, defined as body dimensions and composition, have become

increasingly useful tools for examining an individual’s body parameters to indicate

nutritional status or even the extent and severity of malnutrition in a given population. The

most widely used measurements are the weight and height which may be combined to

examine nutritional status (WHO, 1995).

Growing children need adequate quality of nutrients from food as well as quantity of calories,

they need clean water and adequate sanitation to avoid disease, and they and their mothers

need to be well fed and cared for, particularly in the first 1000 days from conception to age of

2 and beyond. If all of these elements are in place, children anywhere in the world can grow

healthily. In fact, children grow proportionally at remarkably similar rates to the age of five

years old, even if their final attained height in adulthood varies due to other factors (Habicht

et al, 1974). It is known that the socio-economic status of a child (and therefore their ability

to access the elements described above) makes much more difference to their pattern of

growth than their genetic background at this young age.

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This pattern of growth is therefore used to measure a child’s nutritional status. If a child’s

height is significantly below the average of healthy, well-fed children of the same age and

sex, that child is known to suffer from chronic undernutrition due to a lack of one or more of

the elements above, and is known to be stunted (a term used to denote chronic undernutrition.

However, undernutrition is different to undernourishment, which is only about lack of energy

from calories).

The use of weight and height as nutritional indicators are specific to age of individuals. While

for adults, the idea is to explain the body composition, body-mass-index (BMI) is popularly

used. However, for children below the age of 5, the idea is to explain the growth. In that case

the core indicators used are weight-for-age (W/A), height-for-weight (H/W) and height-for-

age (H/A). Growing children need adequate quality of nutrients from food as well as quantity

of calories, they need clean water and adequate sanitation to avoid disease, and they and their

mothers need to be well fed and cared for, particularly in the first 1000 days from conception

to age 2 and beyond. Most Zambians cannot afford a diet that would be considered

nutritious3F

4

The W/A, W/H and H/A are used to compare a child or group of children with a reference

population. This reference was initially based on a population of children in the United States

of America (USA) which was defined by the National Centre for Health Statistics (NCHS)

and accepted by the US Centre for Disease Control in the 1960s. More recently, WHO

completed a new study (WHO Multicentre Growth Reference Study, WHO, 2006), which

provides a new reference point. The study which was conducted among 8,440 children in six

countries (USA, Norway, Brazil, Ghana, Oman, and India) is used on the understanding that

young children of all population groups, regardless of the ethnic grouping or feeding

practices, tend to follow very similar growth patterns under optimal conditions.

Anthropometry measures are constructed from indices and as such are referred to as

‘indicators’. For example the proportion of children below a certain level of weight-for-age is

widely used as an indicator of the community nutritional status. Each of these indices W/A,

W/H and H/A are expressed in terms of Z-scores which is the deviation of the value for an 4 Zambia cost of diet report 2013

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individual from the median value of the reference population divided by the standard

deviation (SD) for the population (WHO, 1995). The Z-score is expressed as:

Z-score or SD score = Observed Value – Median reference value

Standard deviation of the reference population

The indices are generated using the WHO “igrowup” software package. Other expressions of

the anthropometry index are percentile and the percentage median. Studies show that the Z-

score has more advantages than the percentile and the percentage median in that it is able to

detect changes at the extremes of the distribution. In some cases, birth weight is used to

assess nutritional status of new-borns as well as of the mother during pregnancy (See

Chevassus-Agnès, 1999). Ideally, anthropometry measures analyses the response of the body

dimensions, composition and growth to the quantity and composition of food intake. The

measures provide insights into whether an individual or a population needs nutrition

interventions.

Each of the indices, W/A, W/H and H/A provide different nutritional information. The

Height-for-Age index explains the linear growth of a child. A Z-score of below minus two

standard deviations (<-2 SD) from the median of the reference population indicates stunted.

Children falling in this category are considered short for their height, an indication of

chronically malnourished and recurrent illness. Below minus three (<-3 SD) from the median

of the reference population is an indication of severely stunted. Therefore the Height-for-Age

indicator does not only explain the current status but also the future risks as the effect is

chronic. In that case policy interventions should be directed towards long-term solutions.

Weight-for-Height explains acute malnutrition and therefore requires speedy interventions.

Similarly, a Z-score of <-2SD from the median of the reference population means the child is

thin for the height, a condition referred to as being wasted. Children whose Z-score falls

below minus three (<3SD) a considered severely wasted. Inadequate nutrition prior to the

survey or acute illness leading to sudden loss of weight may be the cause of wasted children.

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Studies show that height-for-age has greater sensitivity4F

5 than Height-for-Weight in

identifying children that are likely to die in the next two years (WHO, 1995).

The Weight-for-Age indicates underweight, a condition resulting from both chronic and acute

malnutrition. A Z-score of below minus two standard deviation (<-2 SD) from the reference

population indicates underweight while <-3 SD indicates severe underweight.

5 Sensitivity explains the anthropology indicator in relation to death.

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3. REVIEW OF HUNGER AND MALNUTRITION RATINGS FOR

ZAMBIA

The researchers found the calculation of undernourishment for Zambia problematic. Firstly,

the level of undernourishment calculated by FAO is reported as a population estimate, hence

ignores the variation that may exists in a country. Secondly, a major shortcoming in Zambia

is that national consumption surveys, from which DEC can be derived have not been carried

out consistently with the last survey carried out in 1971. Therefore, in the absence of

consumption survey-derived DEC, the FAO estimated the country’s undernutrition index

based on ‘food balance sheets’ which in Zambia are based on the annual crop forecast survey

results. The total amount of calories available in a country is then divided by the number of

people in the population to derive an average number of calories available per person. The

average number of calories available per person was then compared to the DER with all those

falling below the DER classified as undernourished.

There are evident flaws with this method of calculation, as it is very difficult to get accurate

data on exactly what is produced, particularly in a country like Zambia with many small-scale

farmers who produce crops for their own consumption, and with a tradition of collecting wild

foods if crops fail or during lean seasons. In addition, averaging available calories throughout

the population ignores the fact that calorie availability is in fact unequally distributed

between richer and poorer parts of the population.

Thirdly, Zambia has not had a national food consumption survey since 1971 to obtain

updated information on the DEC. Many available micronutrient surveys are limited to few

areas and only targeting specific geographical areas and limited in scope. In addition, over the

last decade, there have been considerable social, economic and demographic changes in

Zambia impacting the food consumption patterns of both rural and urban populations (Mason

and Jayne 2009; Chapoto et al 2010) but the FAO estimate is static and is not able to capture

population composition and changes. Thus, in the absence of a recent nationally

representative food consumption data to base the DEC it may be difficult to believe the under

nourishment ranking. Also, these surveys are critical to the planning and implementation of

effective nutrition interventions.

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Third, Naiken (2014) also found methodological problems with FAO’s estimation of

undernourishment, stating that the method yields an underestimate of the magnitude of the

prevalence undernourishment in a population. The Naiken (2014) study found that even after

averaging the daily energy intakes of each individual in the reference group over a number of

days to derive DEC, there remained significant differences between the usual DECs of the

individuals in the reference group.

However, the study found that despite these flaws, the undernourishment indicator is a useful

guide to the availability of dietary energy at a population level, and in particular to look at

changes over time. In Zambia, the number of undernourished people by this measure has

risen steadily since 1990, and the proportion of the population going hungry has remained

almost at the same level since 2000.

3.2 What do alternative data sources say about undernutrition in Zambia

In order to triangulate the level of undernutrition in Zambia, the study considered household

provision data from the Rural Agricultural Livelihood surveys (RALS). IAPRI working

together with Ministries of Agriculture and Livestock and the CSO carried out the RALS a

nationally representative survey with 8839 households and 7934 households in 2012 and

2015 respectively. The reference period for the 2012 survey was the 2011-12 agriculture

marketing period while it was 2013-14 for the 2015 survey. To examine the rate of hunger

among the households, the respondents were asked to indicate the number of months in

which they experienced inadequate household food provisions. The results showed that in

2012, 46.7% of the households experienced hunger and this figure remained the same in 2012

as can be seen in Figures 7.

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Figure 7: Percent of Households Experiencing hunger in 2012 and 2015

Source: IAPRI/CSO/MAL, 2012 and 2015

Similarly, the pattern of hunger rates across the provinces has not significantly changed

between 2012 and 2015. In both cases the Eastern Province records the highest prevalence

with 20.6% in 2012 and 18.2% in 2015. Lusaka Province remains the lowest with 3.4% in

2012and 2.0% in 2015 (Figure 8). These variations across the provinces are an indication that

even the FAO food insecurity levels could be a result of effects in some provinces more than

in others.

Figure 8: Hunger rates among Small Holder by Province in 2012 and 2015 (Among

Households Experiencing Hunger in at Least one Month)

Source: IAPRI/CSO/MAL, 2012 and 2015

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Figures 9 and 10 show the months and the percentage of households reporting inadequate

food provisions in those months in 2012 and 2015 respectively. In both periods, hunger is at

its highest in January and February, when household stocks dwindle and food prices rise.

Hunger is at its lowest in April through August, the main harvest period when household

stocks are relatively large and food prices are relatively low. Comparing the percentage of

households reporting inadequate food provisions in 2011-12 to those reporting the same in

2014-15, there appears significant increase in 2014-15 in the months of January and February

when food supplies are at the lowest levels. In 2012-13 about 70.9% and 67.8% of the

households reported inadequate food provisions in December and January respectively, this

figure increased to 97% for both months in 2013-14.

Figure 9: Percentage of Households Experiencing Hunger by Month 2012 (Among

Households Experiencing Hunger in at least one Month)

Source: IAPRI/MAL/CSO 2012

17

Figure 10: Percentage of Households Experiencing Hunger by Month in 2015 (Among

Households Experiencing Hunger in at least one month)

Source: IAPRI/MAL/CSO 2015

The measure of hunger used in this study as defined above may be subjective in the sense that

it entirely depends on the household’s perception of “enough” food. However, food insecurity

measures, such as hunger, can be captured by assessing not only aspects of the availability,

access and utilisation of food, but also how a person feels (e.g., anxiety, worry) and thinks

about it (e.g., perceptions, social acceptability) (Wolfe and Frongillo, 2000). Previous studies

have used number of months in which the household had adequate/inadequate food

provisions to measure food access, hunger or food insecurity (e.g., Bilinsky and Swidale,

2010; Frayne et. al 2010). Such a method of assessing hunger or food insecurity is understood

to be useful for policy analysis and for examining the causes (Mason, 2003).

Secondly, unlike the FAO, SOFI estimates which is based on the entire population, the RALS

survey was rural based. This means that the estimates from the RALS survey maybe different

in the urban areas.

However, there is consistency in the levels of hunger reported by all the three reports. The

FAO, SOFI, the Hunger Index and the figures from the RALS survey show that in general

households do experience undernourishment and inadequate food provisions respectively.

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The rates (48% 41.1% and 46%) are very high, which gives an indication that hunger exists

among rural households and the situation has remained at unacceptable levels and have not

changed between 2011-12 and 2013-2014.

3.3 What the Nutrition Index Means for Zambia

The levels of malnutrition in Zambia are estimated using the LCMS and the ZDHS. Unlike

the hunger estimates which are calculated using the food intake, malnutrition is estimated

using outcomes of food intake, mainly through measuring the height and the weight of the

individuals. We find this method less problematic compared to the measure of food intake.

Over the years, the estimates from the LCMS have been consistent with the estimates from

ZDHS and have proved to be reliable. Of the two surveys, the ZDHS is even more reliable

because trained health workers are involved in collecting Anthropometric measurements

unlike the LCMS.

Even if Zambian, children were not included in the studies that calculated the average height

for healthy, well-fed children, it is highly unlikely that patterns of growth in young children

are particularly different here than everywhere else in the world. It is highly likely that if the

same study were carried out here, the same thing would be found. Children from wealthier

families (and therefore better able to access the elements described above) would reach the

same heights as healthy children elsewhere, irrespective of their ethnicity or background.

3.3.1 Determinants of Nutrition Status

The determinants of nutrition status are food intake and health status. The present nutrition

situation has been attributed to poor food intake which explains inadequate nutrient intake.

Nutrients can be classified as ether macro or micro nutrients. Evidence from elsewhere show

that micronutrient deficiencies affects both women of child bearing age and children under

the age of five in Zambia. The most common of these deficiencies are iron, vitamin A and

zinc which manifests in anemia, night blindness and zinc deficiency. Inadequate intakes of

several micronutrients Zambia are widespread because staple diets are predominantly maize-

based, and intakes of plant based and animal products are low (Mason and Jayne 2009;

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Chapoto et al 2010). As a result, the content and bioavailability of micronutrients such as

iron, zinc and vitamin A are often low in these diets.

According to the 2012 Malaria Indicator survey, anemia affects 55 percent of children under

the age of five and 30 percent of women in the child bearing age, (MoH, 2012). Although the

etiological factors of anemia have not been determined, it is reasonable to assume that in

Zambia, as in many African countries, the primary causes are nutritional deficiencies, malaria

and intestinal worms (Shaw and Friedman 2011). Consequently, more than half (54 percent)

of children under the age of five and 13 percent among women of child bearing age are

vitamin A deficient (NFNC, 2003). According to an indirect method of estimating rates of

zinc deficiency, 38 percent of the population in Zambia is at risk of low zinc intake (IZiNCG,

2004).

In addition, iron and folic acid supplements are very important nutrients that determine ideal

foetal development and health of the child. In Zambia, iron and folic acid are distributed

during antenatal visits, however, compliance has been low with only 59 percent of the women

reporting to have had taken iron tablets for 90 or more days during their last pregnancy (CSO,

2015). This situation has a great contribution to the present nutrition situation in the country;

folic acid and iron are critical in the very early months of pregnancy for foetal growth and for

postnatal growth and development (Stoltzfus 2011). Folic acid and iron are vital for the brain

function, and are part of the brain responsible for complex intellectual performance (Beard

2001, Ebadi, Elsayed, Aly 1994). Iron is a prerequisite in chemicals required for production

of chemicals needed for sending messages to the brain (Beard 2001). It is also important for

growth, development and normal functioning of body cells (Beard 2001). The consequence of

poor iron and folic status are huge. Children with Iron Deficiency Anaemia (IDA) perform

worse than children without IDA on developmental indices both mental (Lozoff, Wolf,

Jimenez 1996) and motor (Harahap, Jahari, Husaini, Saco-Pollit, Pollitt 2000).

There are a number of factors that could explain the present nutrition situation in the country.

Studies conducted elsewhere (Black, Quigg, Hurley, Pepper 2011) have shown that infants,

children, teen-aged girls and pregnant women are in danger of getting insufficient quantities

of iron from the diet alone. Food consumption still remains poor in Zambia as shown by

recent results from the ZDHS. Results show that the majority of women and children have a

dietary diversity lower than normal.

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4. CONCLUSIONS AND RECOMMENDATIONS

This report has discussed the statistics on undernourishment, hunger and malnutrition in

Zambia and the methods and indicators used to calculate them. The study found some

methodological issues in the calculation of undernourishment by FAO although the rates

calculated remain similar to the Hunger Index and the RALS findings. There are two

conclusions researchers can make about the reported undernourishment (hunger) in Zambia.

i. There is hunger in Zambia, and given the figures in the SOFI report as well as the

RALS report, it is likely to be at alarmingly high rates, particularly at lean times of the

year, as seen from the monthly rates, when stores from the previous year are depleted.

If the Zambian government would like to improve food security and reduce hunger in

the country. The government also invest further in helping households to safely store

the harvests, so that these hunger periods are not so severe. The government should

further work towards a more equal economic growth that includes poorest parts of

society in the long term.

ii. One reason Zambia may have ranked so very poorly in this report compared to other

developing countries is lack of good information on food consumption at household

level, thereby giving a low estimate of energy available. If the Zambian government

would like to show progress in combatting hunger, it could support efforts to collect

more regular and more accurate agricultural and food consumption data to improve

these figures.

Going forward, ensuring that children and their families have access to enough diverse and

good-quality foods, clean water and safe sanitation, and that families are taught and

supported to care for their children, must be among the government’s priority areas. Investing

part of the rising tax revenue in ensuring these basic elements are provided would not only

reduce the rates of chronic malnutrition and undernourishment in Zambia, but are important

goals in and of themselves. Improving food security to ensure sufficient calories in the diet

(and therefore avoid undernourishment) is a necessary measure to ensure good nutrition in

Zambia’s population- although it is not on its own sufficient. Sustained political commitment

21

at the highest level is a prerequisite for hunger eradication. It entails placing food security and

nutrition at the top of the political agenda and creating an enabling environment for

improving food security and nutrition (FAO, IFAD, and WFP (2014).

Finally, the poor nutritional situation in Zambia reinforces the urgent need of appropriate and

efficient interventions to mitigate malnutrition in the country. However, to effectively

formulate and evaluate such nutrition interventions, food consumption data and nutrition

status information are urgently required to accelerate the combating of this nutritional

problem.

22

REFERENCES

Beard, J.L. (2001). Iron biology in immune function, muscle metabolism and neuronal

functioning. Journal of Nutrition, 131( Suppl 2), 568s-94s.

Bilinsky, P., and Swindale, A. (2010). Months of adequate household food provisioning

MAHFP) for measurement of food access: Indicator guide (V.4), Washington

D.C.,FHI 360/FANTA.

Black, M. M., Quigg, A. M., Hurley, K. M., and M. R. Pepper. (2011). Iron deficiency and

iron-deficiency anaemia in the first two years of life: Strategies to prevent loss of

developmental potential. Nutr. Rev. 69 Suppl 1:S64-70. doi: 10.1111/j.1753-

4887.2011.00435.x.

CSO (Central Statistical Office). (2015). 2013-14 Zambia Demographic Health Survey

Report. Central Statistical Office, Lusaka, Zambia

Chapoto, A., Govereh, J., Haggblade, S., & Jayne, T. S. (2010). Staple food prices in

Zambia. In preparation for the COMESA policy seminar on “Variation in staple food

prices: Causes, consequence, and policy options”, Maputo, Mozambique pp. 25-26.

Chevassus-Agnès, S. (1999). Anthropometric, health and demographic indicators in assessing

nutritional status and food consumption. FAO Food and Nutrition Division,

Sustainable Development Department.

Ebadi, M., Elsayed, M. A., and Aly, M. H. (1994). The importance of zinc and

metallothionein in the brain. Biol Signals 1994: 3, pp123-6

FAO., IFAD., and WFP. (2014). The state of food insecurity in the world: strengthening the

enabling environment for food security and nutrition. FAO. Rome.

Frayne, Bruce et al. (2010). “The State of Urban Food Insecurity in Southern Africa.” Urban

Food Security Series No. 2. Queen’s University and AFSUN: Kingston and Cape

Town.

23

Harahap, H., Jahari, A. B., Husaini, M., A, Saco-Pollitt, C., and Pollitt, E. (2000).

Effects of an energy and micronutrient supplement on iron deficiency anemia,

physical activity and motor and mental development in undernourished children in

Indonesia. Eur J Clin Nutr. 2000 May; 54 Suppl 2: S114-9.

Habicht, J. T., Yarbrough, C., Martorell, R., Malina, R. M., and Klein, R. E. (1974).

Height and weight standards for preschool children: How relevant are ethnic

differences in growth potential? The Lancet, 303 (7858), 611-615

IAPRI/CSO/MAL. (2012). 2012 Rural agricultural livelihoods survey data (RALS). IAPRI.

Lusaka.

IAPRI/CSO/MAL. (2015). 2015 Rural agricultural livelihoods survey data (RALS). IAPRI.

Lusaka.

IFPRI. Welthungerhilfe., and Concern Worldwide. (2015). 2015 Global hunger index:

Armed conflict and the challenge of hunger. Bonn/Washington DC/Dublin.

International Zinc Nutrition Consultative Group (Izincg) (2004). Assessment Of The

Risk Of Zinc Deficiency In Populations And Options For Its Control. Technical

Document #1. University of California. Davis.

Lozoff, B., Wolf, A. W., and Jimenez, E. (1996). Iron-deficiency anaemia and infant

development: Effects of extended oral iron therapy, Journal of Pediatrics,

129(3):382-9.

Mason, J. B. (2003). Measuring hunger and malnutrition. Keynote paper presented at the

Food Agricultural Organisation of the United Nations symposium.

24

Mason, N., and Jayne, T. S. (2009). Staple food consumption patterns in urban Zambia:

Results from the 2007/2008 urban consumption survey. Food Security Research

Project, Working Paper 42, Lusaka: Michigan State University.

MoH (ministry of Health). (2012). Zambia National Malaria Indicator Survey 2012. Ministry

of Health. Lusaka. Zambia.

Naiken, L. (2014). Methodological issues in the estimation of the prevalence of

undernourishment based on dietary energy consumption data: a review and

clarification. FAO Statistics Division Working Paper Series Ess / 14-03. Fao. Rome.

Italy

NFNC. (2003). Assessment of the risk of zinc deficiency in populations and options for its

control. Vitamin A impact assessment International Zinc Nutrition Consultative

Group(IZiNCG), Hotz C and Brown KH, eds. Food and Nutrition Bulletin 25: S91-

S204, 2004.

Shaw, J. G., and Friedman, J. F. (2011). Iron deficiency anemia: Focus on infectious

diseases in lesser developed countries. Anemia, 2011.

Stoltzfus J. R. (2011). Iron interventions for women and children in low income countries,

American Society for Nutrition. Doi:10.3945/jn.110.

United Nations Children’s Fund (UNICEF). (1990). Strategies of improving nutrition of

Children and women in developing countries. New York: UNICEF.

UNICEF. (2009). Zambia Situation Analysis of Children and Women 2008. UNICEF

Zambia. Lusaka.

WHO. (1995). Physical status: the use and interpretation of anthropometry. Report of a

WHO Committee, Technical Report Series No. 854.

25

WHO. (2006). Multicentre Growth Reference Study Group. WHO Child Growth Standards:

Length/height-for-age, weight-for-age, weight-for-length, weight-for-height and body

mass index-for-age: Methods and development. Geneva: World Health Organization

Wolfe, W., and Frongillo, E. Jr. (2000). Building household food security measurement tools

from the ground up. FANTA Background Paper, Washington, D.C: Food and Nutrition

Technical Assistance Project (FANTA), Academy for Educational Development.

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Annex 1: Nutrition Index and the Challenges that are measured.

Table 3: Anthropometry Index and Challenges Measures

Index Nutritional challenges measured

Weight-for-height Acute malnutrition (wasting)

Height-for-age Chronic malnutrition(Stunting)

Weight-for-age Any protein-energy malnutrition (Underweight)

BMI Under or over nutrition

Birth weight Maternal nutrition and baby survival chances at birth.

Source: WHO 1995

The severity of malnutrition is measured by prevalence rate and differs across the indicators.

For stunting, a prevalence rate of 40% is considered very high while for underweight very

high is anything above 30%. Wasting is considered very high if more than 15% of the

children in a given population have a z-score of <2 SD. The classification for assessing the

severity of malnutrition in a given population is presented in Table 6.

Table 4: Severity of Malnutrition per Indicator

Indicator Severity of malnutrition by prevalence ranges (%)

Low Medium High Very high

Stunting <20 20-29 30-39 >=40

Underweight <10 10-19 20-29 >=30

Wasting < 5 5-9 10-14 >=15

Source: LCMS report 2010.